Sparse Attention for Reducing Transformer Costs in Trading Models

Summary

The document introduces sparse attention as a way to reduce the computation and memory demands of transformer self-attention, whose pairwise sequence comparisons grow quadratically with sequence length. It outlines standard query, key, and value calculations, then describes retaining only selected high-influence sequence elements rather than evaluating every pair. Suggested selection approaches include block patterns, similarity-based clustering, and heuristics. The article’s proposed financial-data adaptation uses attention scores to select a fraction of elements, with a minimum number retained, and allows different attention heads to select different elements.

The discussion includes an MQL5 and OpenCL implementation that modifies an existing multi-head attention layer, then applies the model in a trading expert advisor. It reports that historical testing generated profit, but provides no basis here for treating that outcome as robust evidence. The author cautions that sparse selection may discard useful information, and that parameters must be tuned to the task. Financial data may also resist fixed block structures. Live use would require more thorough evaluation across market conditions and careful risk assessment.

Key ideas

• Standard self-attention compares sequence elements pairwise, making long sequences costly to process.
• Sparse attention reduces work by limiting which sequence elements contribute to each attention calculation.
• Selection can use blocks, similarity, or heuristics, and different attention heads may select different elements.
• The proposed financial application keeps a specified share of high-scoring elements, subject to a minimum count.
• Sparse selection can lose relevant information, and reported historical profitability does not establish live robustness.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.